Use Cases

Predictive Modelling

Evidence

What role for learning health systems in quality improvement within healthcare providers?

Foley, Vale

Abstract

Introduction

Recent decades have seen a focus on quality in healthcare. Quality has been viewed across 6 dimensions—safe, effective, patient-centred, timely, efficient and equitable. As IT has enabled the transformation of other industries, there has been an increasing interest in the potential for learning health systems (LHS) to improve quality in healthcare. We are not aware of any systematic attempt to investigate the potential impacts of different types of LHS on quality within healthcare providers.

Methods

A review of the limited LHS literature informed the topics for 25 expert interviews, 6 focus groups, and 2 site visits. A deductive thematic analysis was conducted to identify the different types of LHSs and their potential impacts across the 6 dimensions of quality.

Results

Six types of LHS were identified—intelligent automation, clinical decision support, predictive models, positive deviance, surveillance, and comparative effectiveness research. The thematic analysis identified that the 6 types of LHS could potentially have a broad range of positive impacts across the 6 dimensions of quality. However, they also identified the potential for negative impacts on quality and highlighted that many of the potential impacts have not been substantiated through rigorous evaluation.

Conclusions

These findings suggest that LHSs may represent an evolution of existing quality improvement techniques or even fundamentally new capabilities within quality improvement. However, they also highlight the need for further research to evaluate the impacts.